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Relevant bibliographies by topics / Neural network controllers

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Academic literature on the topic 'Neural network controllers'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Neural network controllers"

1

Yamada, Takayuki, and Tetsuro Yabuta. "Adaptive Neural Network Controllers for Dynamics Systems." Journal of Robotics and Mechatronics 2, no. 4 (1990): 245–57. http://dx.doi.org/10.20965/jrm.1990.p0245.

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Many studies such as Kawato's work have been undertaken in order to apply both the flexibility and learning ability of neural networks to dynamic system controllers. However, their characteristics have not yet been completely clarified. On the other hand, many studies have established conventional control theories such as adaptive control. If we can clarify the relationship between neural network controllers and adaptive controllers, the two control algorithms will be developed considerably by making use of the advantages of each. Therefore, this paper proposes a neural network direct controller in order to construct an interface between neural network and conventional control theories. This paper also proposes an open loop type of controller in order to realize inverse dynamics using only the neural network. Analytical approaches prove the local stability of the proposed controllers. Simulated and experimental results verify their realization and confirm their characteristics. This paper also discusses the relationship between neural network controllers and adaptive controllers.

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Günther, Johannes, Elias Reichensdörfer, Patrick M. Pilarski, and Klaus Diepold. "Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison." PLOS ONE 15, no. 12 (2020): e0243320. http://dx.doi.org/10.1371/journal.pone.0243320.

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Modern automation systems largely rely on closed loop control, wherein a controller interacts with a controlled process via actions, based on observations. These systems are increasingly complex, yet most deployed controllers are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but their simplicity is at odds with the robustness required to reliably control complex processes. Modern machine learning techniques offer a way to extend PID controllers beyond their linear control capabilities by using neural networks. However, such an extension comes at the cost of losing stability guarantees and controller interpretability. In this paper, we examine the utility of extending PID controllers with recurrent neural networks—–namely, General Dynamic Neural Networks (GDNN); we show that GDNN (neural) PID controllers perform well on a range of complex control systems and highlight how they can be a scalable and interpretable option for modern control systems. To do so, we provide an extensive study using four benchmark systems that represent the most common control engineering benchmarks. All control environments are evaluated with and without noise as well as with and without disturbances. The neural PID controller performs better than standard PID control in 15 of 16 tasks and better than model-based control in 13 of 16 tasks. As a second contribution, we address the lack of interpretability that prevents neural networks from being used in real-world control processes. We use bounded-input bounded-output stability analysis to evaluate the parameters suggested by the neural network, making them understandable for engineers. This combination of rigorous evaluation paired with better interpretability is an important step towards the acceptance of neural-network-based control approaches for real-world systems. It is furthermore an important step towards interpretable and safely applied artificial intelligence.

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Scott, Gary M., Jude W. Shavlik, and W. Harmon Ray. "Refining PID Controllers Using Neural Networks." Neural Computation 4, no. 5 (1992): 746–57. http://dx.doi.org/10.1162/neco.1992.4.5.746.

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The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting the MANNCON (Multivariable Artificial Neural Network Control) algorithm by which the mathematical equations governing a PID (Proportional-Integral-Derivative) controller determine the topology and initial weights of a network, which is further trained using backpropagation. We apply this method to the task of controlling the outflow and temperature of a water tank, producing statistically significant gains in accuracy over both a standard neural network approach and a nonlearning PID controller. Furthermore, using the PID knowledge to initialize the weights of the network produces statistically less variation in test set accuracy when compared to networks initialized with small random numbers.

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Lippe, Wolfram-M., Steffen Niendieck, and Andreas Tenhagen. "On the Optimization of Fuzzy-Controllers by Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (1999): 158–63. http://dx.doi.org/10.20965/jaciii.1999.p0158.

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Methods are known for combining fuzzy-controllers with neural networks. One of the reasons of these combinations is to work around the fuzzy controllers’ disadvantage of not being adaptive. It is helpful to represent a given fuzzy controller by a neural network and to have rules adapted by a special learning algorithm. Some of these methods are applied in the NEFCONmode or the model of Lin and Lee. Unfortunately, none adapts all fuzzy-controller components. We suggest a new model enabling the user to represent a given fuzzy controller by a neural network and adapt its components as desired.

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Darsivan, Fadly Jashi, Wahyudi Martono, and Waleed F. Faris. "Active Engine Mounting Control Algorithm Using Neural Network." Shock and Vibration 16, no. 4 (2009): 417–37. http://dx.doi.org/10.1155/2009/257480.

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This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

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Woodford, Grant W., and Mathys C. du Plessis. "Complex Morphology Neural Network Simulation in Evolutionary Robotics." Robotica 38, no. 5 (2019): 886–902. http://dx.doi.org/10.1017/s0263574719001140.

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SUMMARYThis paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dimensional problem specified in this work.

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Ivanov, Radoslav, Kishor Jothimurugan, Steve Hsu, Shaan Vaidya, Rajeev Alur, and Osbert Bastani. "Compositional Learning and Verification of Neural Network Controllers." ACM Transactions on Embedded Computing Systems 20, no. 5s (2021): 1–26. http://dx.doi.org/10.1145/3477023.

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Recent advances in deep learning have enabled data-driven controller design for autonomous systems. However, verifying safety of such controllers, which are often hard-to-analyze neural networks, remains a challenge. Inspired by compositional strategies for program verification, we propose a framework for compositional learning and verification of neural network controllers. Our approach is to decompose the task (e.g., car navigation) into a sequence of subtasks (e.g., segments of the track), each corresponding to a different mode of the system (e.g., go straight or turn). Then, we learn a separate controller for each mode, and verify correctness by proving that (i) each controller is correct within its mode, and (ii) transitions between modes are correct. This compositional strategy not only improves scalability of both learning and verification, but also enables our approach to verify correctness for arbitrary compositions of the subtasks. To handle partial observability (e.g., LiDAR), we additionally learn and verify a mode predictor that predicts which controller to use. Finally, our framework also incorporates an algorithm that, given a set of controllers, automatically synthesizes the pre- and postconditions required by our verification procedure. We validate our approach in a case study on a simulation model of the F1/10 autonomous car, a system that poses challenges for existing verification tools due to both its reliance on LiDAR observations, as well as the need to prove safety for complex track geometries. We leverage our framework to learn and verify a controller that safely completes any track consisting of an arbitrary sequence of five kinds of track segments.

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Joshi, Girisha, and Pinto Pius A J. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1177. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.

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Wilamowski, B. M., J. Binfet, and M. O. Kaynak. "VLSI Implementation of Neural Networks." International Journal of Neural Systems 10, no. 03 (2000): 191–97. http://dx.doi.org/10.1142/s012906570000017x.

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Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries. Two neural networks were designed in 1.5 μm technology. Using adequate approximation functions solved the problem of activation function. With this approach, trained networks were characterized by very small errors. Unfortunately, when the weights were quantized, errors were increased by an order of magnitude. However, even though the errors were enlarged, the results obtained from neural network hardware implementations were superior to the results obtained with fuzzy system approach.

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Clitan, Iulia, Mihai Abrudean, and Vlad Mureşan. "Design of Neural Network Controllers for the Horizontal Positioning of an Industrial Manipulator." Applied Mechanics and Materials 555 (June 2014): 281–87. http://dx.doi.org/10.4028/www.scientific.net/amm.555.281.

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This paper presents the design of neural network controllers for the electro-hydraulically driven positioning system of an industrial manipulator. The manipulator is represented by an unloading machine that extracts the billets from a rotary hearth furnace. The design of a Narma-L2 controller and a Model-reference controller is presented. Neural network controllers can be used for the modeling and control of dynamical systems as long as a suitable neural network is chosen. The obtained controllers are compared on the basis of overall performances. The assessment of the results is done by means of simulation.

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Dissertations / Theses on the topic "Neural network controllers"

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Balaam, Andy. "Exploring developmental dynamics in evolved neural network controllers." Thesis, University of Sussex, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426199.

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Kimball, Nicholas. "Utilizing Trajectory Optimization In The Training Of Neural Network Controllers." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2071.

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Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum system, it learns an effective policy up to three times faster than the other algorithms. In the cartpole system, it learns an effective policy up to nearly fifteen times faster than the other algorithms.

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Chan, Yat-fei. "Neurofuzzy network based adaptive nonlinear PID controllers." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43958357.

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Chan, Yat-fei, and 陳一飛. "Neurofuzzy network based adaptive nonlinear PID controllers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43958357.

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Hutt, Benjamin David. "Evolving artificial neural network controllers for robots using species-based methods." Thesis, University of Reading, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270831.

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Rathbone, Kevin. "Evolving visually guided neural network robot arm controllers for lifetime learning." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327646.

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Smith, Bradley R. "Neural Network Enhancement of Closed-Loop Controllers for Ill-Modeled Systems with Unknown Nonlinearities." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29607.

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The nonlinearities of a nonlinear system can degrade the performance of a closed-loop system. In order to improve the performance of the closed-loop system, an adaptive technique, using a neural network, was developed. A neural network is placed in series between the output of the fixed-gain controller and the input into the plant. The weights are initialized to values that result in a unity gain across the neural network, which is referred to as a "feed-through neural network." The initial unity gain causes the output of the neural network to be equal to the input of neural network at the beginning of the convergence process. The result is that the closed-loop system's performance with the neural network is, initially, equal to the closed-loop system's performance without the neural network. As the weights of the neural network converge, the performance of the system improves. However, the back propagation algorithm was developed to update the weights of the feed-forward neural network in the open loop. Although the back propagation algorithm converged the weights in the closed loop, it worked very slowly. Two new update algorithms were developed for converging the weights of the neural network inside the closed-loop. The first algorithm was developed to make the convergence process independent of the plants dynamics and to correct for the effects of the closed loop. The second algorithm does not eliminate the effects of the plant's dynamics, but still does correct for the effects of the closed loop. Both algorithms are effective in converging the weights much faster than the back propagation algorithm. All of the update algorithms have been shown to work effectively on stable and unstable nonlinear plants.<br>Ph. D.

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Massera, Gianluca. "Evolution of grasping behaviour in anthropomorphic robotic arms with embodied neural controllers." Thesis, University of Plymouth, 2012. http://hdl.handle.net/10026.1/1172.

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The works reported in this thesis focus upon synthesising neural controllers for anthropomorphic robots that are able to manipulate objects through an automatic design process based on artificial evolution. The use of Evolutionary Robotics makes it possible to reduce the characteristics and parameters specified by the designer to a minimum, and the robot’s skills evolve as it interacts with the environment. The primary objective of these experiments is to investigate whether neural controllers that are regulating the state of the motors on the basis of the current and previously experienced sensors (i.e. without relying on an inverse model) can enable the robots to solve such complex tasks. Another objective of these experiments is to investigate whether the Evolutionary Robotics approach can be successfully applied to scenarios that are significantly more complex than those to which it is typically applied (in terms of the complexity of the robot’s morphology, the size of the neural controller, and the complexity of the task). The obtained results indicate that skills such as reaching, grasping, and discriminating among objects can be accomplished without the need to learn precise inverse internal models of the arm/hand structure. This would also support the hypothesis that the human central nervous system (cns) does necessarily have internal models of the limbs (not excluding the fact that it might possess such models for other purposes), but can act by shifting the equilibrium points/cycles of the underlying musculoskeletal system. Consequently, the resulting controllers of such fundamental skills would be less complex. Thus, the learning of more complex behaviours will be easier to design because the underlying controller of the arm/hand structure is less complex. Moreover, the obtained results also show how evolved robots exploit sensory-motor coordination in order to accomplish their tasks.

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Liu, Jingrong. "Design and Analysis of Intelligent Fuzzy Tension Controllers for Rolling Mills." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/848.

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This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discussed. To improve the performance of the fuzzy controller, a dynamic fuzzy controller is proposed. By switching the fuzzy controller elements in relation to the step response, both transient and stationary performances are enhanced. To endow the fuzzy controller with intelligence of generalization, flexibility and adaptivity, self-learning techniques are introduced to obtain fuzzy controller parameters. With the inclusion of supervision and concern for conventional control criteria, the parameters of the fuzzy inference system are tuned by a backward propagation algorithm or their optimal values are located by means of a genetic algorithm. In simulations, the neuro-fuzzy tension controller exhibits the real-time applicability, while the genetic fuzzy tension controller reveals an outstanding global optimization ability.

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Mohammadzadeh, Soroush. "System identification and control of smart structures: PANFIS modeling method and dissipativity analysis of LQR controllers." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/868.

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"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy inference system is proposed for fast and accurate modeling of time-dependent behavior of a structure integrated with a smart damper. Since a smart damper can only dissipate energy from structures, a challenge is to evaluate the dissipativity of optimal control methods for smart dampers to decide if the optimal controller can be realized using the smart damper. Therefore, a generalized deterministic definition for dissipativity is proposed and a commonly used controller, LQR is proved to be dissipative. Examples are provided to illustrate the effectiveness of the proposed modeling algorithm and evaluating the dissipativity of LQR control method. These examples illustrate the effectiveness of the proposed modeling algorithm and dissipativity of LQR controller."

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Books on the topic "Neural network controllers"

1

Blake, Joseph. Neural network controllers: Software implementation and a hardware implementation based on a reconfigurable computing application. The Author], 1996.

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Haynes, B. P. A neural network adaptive controller for non-linear systems. University of Portsmouth, Faculty of Technology, 1997.

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Jorgensen, Charles C. Distributed memory approaches for robotic neural controllers. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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Jorgensen, Charles C. Development of a sensor coordinated kinematic model for neural network controller training. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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Rylatt, R. Mark. Investigations into controllers for adaptive autonomous agents based on artificial neural networks. De Montfort University, 2001.

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Two neural network algorithms for designing optimal terminal controllers with open final-time. NASA Ames Research Center, 1992.

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Adaptive Neural Network Controller for ATM Traffic. Storming Media, 1996.

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Denise, Taylor Lynore, and United States. National Aeronautics and Space Administration., eds. Artificial neural network implementation of a near-ideal error prediction controller. Dept. of Electrical Engineering, School of Engineering and Applied Science, University of Virginia, 1992.

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Artificial neural network implementation of a near-ideal error prediction controller. Dept. of Electrical Engineering, School of Engineering and Applied Science, University of Virginia, 1992.

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Supervised Sequence Labelling With Recurrent Neural Networks. Springer, 2012.

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Book chapters on the topic "Neural network controllers"

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Pham, Duc Truong, and Xing Liu. "Neural Network Controllers." In Neural Networks for Identification, Prediction and Control. Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3244-8_6.

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Zhao, Hengjun, Xia Zeng, Taolue Chen, Zhiming Liu, and Jim Woodcock. "Learning Safe Neural Network Controllers with Barrier Certificates." In Dependable Software Engineering. Theories, Tools, and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62822-2_11.

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Lichtensteiger, Lukas, and Rolf Pfeifer. "An Optimal Sensor Morphology Improves Adaptability of Neural Network Controllers." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_138.

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Ferreira, Enrique D., and Bruce H. Krogh. "Switching controllers based on neural network estimates of stability regions and controller performance." In Hybrid Systems: Computation and Control. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64358-3_36.

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Gross, Clemens, and Hendrik Voelker. "A Comparison of Tuning Methods for PID-Controllers with Fuzzy and Neural Network Controllers." In Cyber-Physical Systems and Control. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34983-7_8.

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Pujol, João Carlos Figueira, and Riccardo Poli. "Dual network representation applied to the evolution of neural controllers." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0040815.

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Hussin, Mahmoud F., Badr M. Abouelnasr, and Amin A. Shoukry. "Comparative Study of Neural Network Controllers for Nonlinear Dynamic Systems." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45486-1_30.

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Yang, Zhengfeng, Yidan Zhang, Wang Lin, et al. "An Iterative Scheme of Safe Reinforcement Learning for Nonlinear Systems via Barrier Certificate Generation." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_22.

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AbstractIn this paper, we propose a safe reinforcement learning approach to synthesize deep neural network (DNN) controllers for nonlinear systems subject to safety constraints. The proposed approach employs an iterative scheme where a learner and a verifier interact to synthesize safe DNN controllers. The learner trains a DNN controller via deep reinforcement learning, and the verifier certifies the learned controller through computing a maximal safe initial region and its corresponding barrier certificate, based on polynomial abstraction and bilinear matrix inequalities solving. Compared with the existing verification-in-the-loop synthesis methods, our iterative framework is a sequential synthesis scheme of controllers and barrier certificates, which can learn safe controllers with adaptive barrier certificates rather than user-defined ones. We implement the tool SRLBC and evaluate its performance over a set of benchmark examples. The experimental results demonstrate that our approach efficiently synthesizes safe DNN controllers even for a nonlinear system with dimension up to 12.

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Ivanov, Radoslav, Taylor Carpenter, James Weimer, Rajeev Alur, George Pappas, and Insup Lee. "Verisig 2.0: Verification of Neural Network Controllers Using Taylor Model Preconditioning." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_11.

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AbstractThis paper presents Verisig 2.0, a verification tool for closed-loop systems with neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop a Taylor-model-based reachability algorithm through Taylor model preconditioning and shrink wrapping. Furthermore, we provide a parallelized implementation that allows Verisig 2.0 to efficiently handle larger NNs than existing tools can. We provide an extensive evaluation over 10 benchmarks and compare Verisig 2.0 against three state-of-the-art verification tools. We show that Verisig 2.0 is both more accurate and faster, achieving speed-ups of up to 21x and 268x against different tools, respectively.

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Zhao, Longyang, Xiao Zhu, Haoming Yang, and Xuanju Dang. "Design of Hybrid Controllers Based on Radial Basis Function Neural Network." In Lecture Notes in Electrical Engineering. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4796-1_26.

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Conference papers on the topic "Neural network controllers"

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Scott, R. W., and D. J. Collins. "Neural network adaptive controllers." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137872.

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Kumar, Manish, and Devendra P. Garg. "Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59589.

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Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.

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Veloz, Alejandro, Juan C. Romero Quintini, Mónica Parada, and Sergio E. Diaz. "Experimental Testing of a Magnetically Levitated Rotor With a Neural Network Controller." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69120.

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Magnetic bearings represent a solution for high rotating speeds and sterile environments where lubrication fluids could contaminate. They can also be used in systems where maintenance is difficult or inaccessible, because they don’t require auxiliary lubrication systems and don’t suffer mechanic wear as they work with no contact between rotor and bearing stator. An important part of magnetic bearings is the controller; which is needed to stabilize the system. This controller is generally a PID in which tuning and/or filters design can be complicated for not well known systems. This work presents results of the development of a neural network controller, which is potentially easier to implement, to control the position of a magnetically suspended rotor. The proposed controller is based in the identification of the system inverse model. This is achieved first by implementing a simple PID capable of levitating the rotor, and then some excitations are applied to the rotor in order to acquire data of the position of the rotor and current in the actuators. Current and position data is used to train the artificial neural network for the controller. The controller was implemented in a numerical model and also in an experimental system with a rotor of 1.06kg and 300mm in length. The implementation of SISO, MISO and MIMO neural controllers (both with offline and online training) and a conventional PID with neural network compensation are compared. Structures and architectures of networks are shown. Vibration responses to: a constant force; a controlled impact and a constant acceleration ramp between 0 and 12500rpm are compared. Results in both, numeric model and experimental system, show that neural network controllers are capable of hovering the rotor and control vibrations. Peak-Peak amplitudes vs. rpm plots are similar to a conventional PID. In most cases, the neural network controllers show amplitudes slightly lowers on low frequencies and slightly higher on higher frequencies, except the conventional PID with neural network compensation case, were the system responses as with higher damping. Finally, a discussion is made about future steps in research to improve implementation of a neural controller that is potentially simpler and faster in terms of tuning and with a comparable performance to a conventional magnetic bearing PID controller.

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Hong Wang. "Towards stable neural network controllers." In International Conference on Control '94. IEE, 1994. http://dx.doi.org/10.1049/cp:19940184.

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Dieulot, J. Y., P. Borne, and W. Mrizak. "Composite Multimodel and Neural Network Controllers." In Multiconference on "Computational Engineering in Systems Applications. IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.4281705.

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Kis, Karol, Martin Klauco, and Alajos Meszaros. "Neural Network Controllers in Chemical Technologies." In 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE). IEEE, 2020. http://dx.doi.org/10.1109/sose50414.2020.9130425.

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Batayneh, Wafa, and Nash’at Nawafleh. "Comparative Study of DC Motor Speed Control Using Neural Networks and Fuzzy Logic Controller." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51362.

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Abstract:

This paper demonstrates the importance of the intelligent controllers over the conventional methods. A speed control of the DC motor is developed using both Neural Networks and Fuzzy logic controller in MATLAB environment as intelligent controllers. In addition a conventional PID controller is developed for comparison purposes. Both intelligent controllers are designed based on the simulation results of the nonlinear equations in addition to the expert pre knowledge of the system. The output response of the system is obtained using the two types of the intelligent controllers, in addition to the conventional PID controller. The performance of the designed Neural Networks, Fuzzy logic controller and the PID controller is compared and investigated. Finally, the results show that the neural network has minimum overshoot, and minimum steady state parameters. This shows more efficiency of the intelligent controllers over the conventional PID controller. Also it shows that Neural Networks is better than Fuzzy logic controller in terms of over shoot and rising time. At the end of this paper an implementation of Graphical User Interface (GUI) method is developed. The main purpose of the GUI is to give the users a chance to use the program in a simple way without the need to understand the program languages.

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Shukla, D., D. M. Dawson, and F. W. Paul. "Multiple neural network based DCAL controllers using orthonormal activation function neural networks." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.610811.

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Patre, Parag M., Shubhendu Bhasin, Zachary D. Wilcox, and Warren E. Dixon. "Composite adaptation for neural network-based controllers." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400453.

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Calise, Anthony, and mann Sharma. "Neural network augmentation of existing linear controllers." In AIAA Guidance, Navigation, and Control Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-4163.

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Reports on the topic "Neural network controllers"

1

Braganza, D., D. M. Dawson, I. D. Walker, and N. Nath. Neural Network Grasping Controller for Continuum Robots. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada462583.

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Patro, S., and W. J. Kolarik. Integrated evolutionary computation neural network quality controller for automated systems. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/350895.

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Vitela, J. E., U. R. Hanebutte, and J. Reifman. An artificial neural network controller for intelligent transportation systems applications. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/219376.

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Russell, Chris A., and Glenn F. Wilson. Application of Artificial Neural Networks for Air Traffic Controller Functional State Classification. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada404631.

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Blinov, D. O., A. I. Fomin, and A. A. Chibin. Neural network model for determining the values of the indicator of the effectiveness of the impact of controlled means on air objects. OFERNiO, 2021. http://dx.doi.org/10.12731/ofernio.2021.24804.

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Nikiforov, Vladimir. The use of composite materials in smart medical equipment, including with innovative laser systems, controlled and controlled complexes with elements of artificial intelligence and artificial neural networks. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2133.

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Nikiforov, Vladimir. The use of composite materials in smart medical equipment, including usage of innovative laser systems, controlled and monitored by complexes with elements of artificial intelligence and artificial neural networks. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2171.

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